class: center, middle, inverse, title-slide # SIVOCS --- <style> .center2 { margin: 0; position: absolute; top: 50%; left: 50%; -ms-transform: translate(-50%, -50%); transform: translate(-50%, -50%); } .large { font-size: 130% } .small { font-size: 70% } .remark-slide-content.hljs-default { border-top: 60px solid #23373B; } .remark-slide-content > h1 { font-size: 30px; margin-top: -75px; } </style> ## B1: How familiar are you with the concept of “social innovation” -- **H**: The familiarity with the concept of SI depends on the field of research. --- # B1 (NEW): Kruskal-Wallis test: SI familiarity depends on sci. domains? ``` ## ## Bartlett test of homogeneity of variances ## ## data: familiarWithSI.response. by domain ## Bartlett's K-squared = 12.931, df = 2, p-value = 0.001556 ``` ``` ## ## Kruskal-Wallis rank sum test ## ## data: familiarWithSI.response. by domain ## Kruskal-Wallis chi-squared = 45.694, df = 2, p-value = 1.196e-10 ``` * Mean ranks of the groups are not the same, SI fam. depends on domain --- # Pairwise Wilcoxon ``` ## ## Pairwise comparisons using Wilcoxon rank sum test with continuity correction ## ## data: data$familiarWithSI.response. and data$domain ## ## Biology and Medicine ## Humanities and Social Sciences 2.0e-09 ## Math., Natur. and Eng. Sci. 1 ## Humanities and Social Sciences ## Humanities and Social Sciences - ## Math., Natur. and Eng. Sci. 2.8e-07 ## ## P value adjustment method: bonferroni ``` --- # B1: *Familiarity with SI* across scientific domains <br> <img src="data:image/png;base64,#7780_int_meeting_files/figure-html/unnamed-chunk-4-1.svg" width="864" style="display: block; margin: auto;" /> <br> <br> * *Familiarity with SI* differs across scientific domains (Kruskal-Wallis p-value < 0.05) * *Biology and Medicine* and *Math., Natur, and Eng. Sci.* are **not** stat. significantly different (pairwise Wilcoxon p-value > 0.05) * *Humanities and Social Sciences* are significantly different than the others (pairwise t-test with each: p < 0.05) --- class:clear ## H: Generating deeper/better understanding of a specific social issue depends on (the level of) transdisciplinary involvement of citizens ``` ## ## Shapiro-Wilk normality test ## ## data: data.questions$Impactstatements.understanding. ## W = 0.55565, p-value < 2.2e-16 ``` ``` ## ## Shapiro-Wilk normality test ## ## data: data.questions$groupsInvolved.citiz. ## W = 0.5649, p-value < 2.2e-16 ``` --- ``` ## ## Kruskal-Wallis rank sum test ## ## data: Impactstatements.understanding. by groupsInvolved.citiz. ## Kruskal-Wallis chi-squared = 8.2846, df = 2, p-value = 0.01589 ``` * Different involvement levels make difference. --- ``` ## ## Spearman's rank correlation rho ## ## data: data.questions$Impactstatements.understanding. and data.questions$groupsInvolved.citiz. ## S = 5945746, p-value = 0.0041 ## alternative hypothesis: true rho is not equal to 0 ## sample estimates: ## rho ## 0.1535068 ``` ---
--- # D1: Motivation Types ## H: Motivation Types depend on sci. domains --- ###### Phenomenon .pull-left[ <img src="data:image/png;base64,#7780_int_meeting_files/figure-html/unnamed-chunk-10-1.svg" width="1152" /> ] .pull-right[ ``` ## ## Shapiro-Wilk normality test ## ## data: data.questions$motivation.pheno. ## W = 0.65462, p-value < 2.2e-16 ``` ``` ## ## Kruskal-Wallis rank sum test ## ## data: motivation.pheno. by domain ## Kruskal-Wallis chi-squared = 0.037102, df = 2, p-value = 0.9816 ``` * No stat. sig. difference between domains ] --- --- # Dim. Red.: PCA ``` ## [1] 0 ``` <img src="data:image/png;base64,#7780_int_meeting_files/figure-html/unnamed-chunk-13-1.svg" width="1152" /> --- # Dim. Red.: PCA <img src="data:image/png;base64,#7780_int_meeting_files/figure-html/unnamed-chunk-14-1.svg" width="1152" /> --- # Dim. Red.: PCA, imp. features in PC1 ``` ## groupsInvolved.citiz. targetGroupsGoals.empower. ## 0.2332093 0.2319459 ## impactTargetGroup.welfare. impactTargetGroup.socgr. ## 0.2288645 0.2283431 ## dissChannels.events. impactTargetGroup.pub. ## 0.2203846 0.2054193 ## benefitForNonAcademy targetGroupsGoals.diversity. ## 0.1970605 0.1938333 ## contribToSI.rate. groupsInvolved.media. ## 0.1887885 0.1885870 ``` --- # Dim. Red.: Factor Analysis <img src="data:image/png;base64,#7780_int_meeting_files/figure-html/unnamed-chunk-16-1.svg" width="1152" /> ``` ## Parallel analysis suggests that the number of factors = 10 and the number of components = NA ``` --- # Dim. Red.: FActor Analysis ``` ## Factor Analysis using method = minres ## Call: fa(r = data.num_questions, nfactors = 10, rotate = "oblimin", ## fm = "minres") ## Standardized loadings (pattern matrix) based upon correlation matrix ## MR2 MR1 MR7 MR3 MR6 MR9 MR5 MR4 ## transdisciplinaryExp.rate. -0.07 0.27 0.45 -0.05 -0.26 0.24 -0.01 0.13 ## familiarWithSI.response. -0.01 0.29 0.37 -0.14 0.12 0.02 -0.14 0.05 ## contribToSI.rate. 0.02 0.04 0.00 -0.03 0.10 0.89 0.01 -0.05 ## motivation.pheno. 0.00 0.08 -0.02 0.05 0.03 0.04 0.04 0.23 ## motivation.prob. -0.04 -0.02 0.05 -0.03 0.01 0.10 0.04 -0.13 ## motivation.welfare. 0.00 0.17 0.19 -0.02 0.02 0.34 -0.11 -0.54 ## benefitForNonAcademy -0.09 0.09 0.29 0.07 0.18 0.29 0.05 -0.38 ## groupsInvolved.res. -0.08 0.16 0.35 0.07 -0.16 0.10 -0.06 0.18 ## groupsInvolved.busi. -0.05 0.00 0.45 0.06 0.14 -0.18 0.02 -0.15 ## groupsInvolved.civsoc. 0.02 0.52 0.09 -0.14 0.35 -0.13 0.00 0.01 ## groupsInvolved.policy. -0.09 0.10 -0.11 0.01 0.77 0.01 -0.02 -0.01 ## groupsInvolved.citiz. -0.04 0.62 0.18 -0.01 0.08 -0.10 -0.01 0.07 ## groupsInvolved.media. 0.04 0.10 0.08 0.25 0.29 0.11 0.02 0.18 ## groupsInvolved.welfare. -0.04 0.50 0.02 -0.03 0.14 -0.01 0.09 -0.14 ## targetGroupsGoals.socneeds. -0.04 0.55 -0.05 -0.09 0.06 0.05 -0.06 -0.14 ## targetGroupsGoals.socgroups. -0.02 0.71 -0.21 0.01 -0.11 0.15 -0.07 0.04 ## targetGroupsGoals.improve. 0.14 0.23 0.07 0.00 -0.08 -0.04 -0.18 -0.29 ## targetGroupsGoals.empower. 0.06 0.44 0.02 0.05 0.21 0.09 -0.12 0.06 ## targetGroupsGoals.diversity. -0.01 0.33 0.06 0.11 0.20 0.02 0.05 0.44 ## concepts.pub. -0.03 -0.01 -0.04 0.42 0.00 -0.11 0.08 -0.09 ## concepts.data. 0.02 -0.15 -0.03 0.59 -0.02 -0.10 -0.12 0.03 ## concepts.code. 0.01 -0.03 0.09 0.47 -0.10 -0.18 -0.02 -0.03 ## concepts.infra. 0.05 -0.03 0.10 0.29 0.00 -0.09 0.00 0.06 ## concepts.review. 0.01 -0.12 0.24 0.04 0.10 0.04 0.03 0.29 ## impactTargetGroup.pub. -0.04 0.02 0.55 0.14 0.06 0.29 -0.02 -0.14 ## impactTargetGroup.busi. 0.01 -0.10 0.71 0.07 0.05 -0.12 -0.02 -0.08 ## impactTargetGroup.socgr. -0.01 0.65 0.06 0.10 0.07 0.10 -0.02 -0.05 ## impactTargetGroup.welfare. 0.00 0.32 0.24 0.00 0.14 0.18 0.14 -0.07 ## impactTargetGroup.civsoc. 0.04 0.29 0.18 -0.04 0.32 0.12 -0.13 0.07 ## impactTargetGroup.policy. 0.01 -0.07 0.24 -0.08 0.61 0.26 -0.14 0.06 ## impactTargetGroup.acad. 0.08 -0.11 0.35 0.09 -0.05 0.12 -0.04 0.26 ## adoptByPolicy.rate. 0.55 0.01 0.12 -0.08 0.04 -0.15 0.19 0.04 ## Impactstatements.capab. 0.81 -0.04 -0.09 0.06 -0.03 0.05 -0.02 0.01 ## Impactstatements.emanc. 0.89 -0.06 0.02 0.01 0.00 -0.02 0.05 -0.02 ## Impactstatements.understanding. 0.92 0.03 0.00 0.01 0.00 0.00 -0.01 -0.03 ## Impactstatements.mitig. 0.88 0.07 0.04 -0.01 0.00 0.03 0.10 0.03 ## Impactstatements.unknown. 0.83 0.01 0.00 -0.04 0.02 -0.01 -0.06 0.01 ## Impactstatements.unaddressed. 0.46 0.01 -0.11 -0.03 -0.07 0.28 -0.09 -0.01 ## dissChannels.peer. 0.13 -0.06 -0.06 0.13 0.08 -0.04 -0.09 -0.02 ## dissChannels.mono. -0.10 0.14 -0.02 -0.04 0.09 0.28 0.09 0.38 ## dissChannels.conf. 0.04 0.14 0.00 -0.01 -0.02 -0.13 -0.11 0.05 ## dissChannels.policy. 0.05 0.03 0.01 0.07 0.48 -0.01 -0.09 -0.01 ## dissChannels.trad. 0.00 0.01 -0.01 0.46 0.20 0.19 0.00 0.14 ## dissChannels.prof. -0.06 0.14 0.05 0.19 0.10 0.33 0.13 0.10 ## dissChannels.web. -0.10 0.12 0.02 0.44 -0.04 0.01 0.12 -0.07 ## dissChannels.socmed. -0.03 0.01 0.15 0.38 0.05 0.07 0.07 -0.05 ## dissChannels.platf. 0.08 0.15 0.06 0.48 -0.09 -0.05 -0.09 0.00 ## dissChannels.consult. -0.05 0.14 0.17 0.21 0.03 0.09 -0.06 0.06 ## dissChannels.events. -0.08 0.40 0.03 0.15 0.17 0.16 0.11 -0.03 ## dissChannels.public. 0.03 0.10 0.06 0.36 0.00 0.23 0.17 0.02 ## scalabilityRating.up. -0.08 0.04 0.02 -0.03 0.01 0.06 0.71 0.08 ## scalabilityRating.out. 0.12 -0.02 -0.05 -0.02 -0.05 0.04 0.81 -0.01 ## scalabilityRating.deep. 0.17 -0.06 0.04 0.04 -0.04 -0.12 0.62 -0.07 ## MR10 MR8 h2 u2 com ## transdisciplinaryExp.rate. -0.04 0.02 0.42 0.579 3.3 ## familiarWithSI.response. -0.02 0.07 0.40 0.596 3.0 ## contribToSI.rate. 0.16 0.02 1.00 -0.002 1.1 ## motivation.pheno. 0.65 -0.08 0.49 0.509 1.3 ## motivation.prob. 0.76 0.04 0.67 0.327 1.1 ## motivation.welfare. 0.12 0.05 0.74 0.257 2.5 ## benefitForNonAcademy -0.09 0.04 0.57 0.428 3.9 ## groupsInvolved.res. 0.01 0.25 0.33 0.667 4.1 ## groupsInvolved.busi. 0.05 0.04 0.29 0.711 1.9 ## groupsInvolved.civsoc. 0.02 -0.19 0.54 0.458 2.5 ## groupsInvolved.policy. 0.06 0.02 0.67 0.331 1.1 ## groupsInvolved.citiz. -0.02 0.09 0.51 0.494 1.3 ## groupsInvolved.media. -0.07 0.21 0.36 0.643 4.6 ## groupsInvolved.welfare. -0.02 0.22 0.42 0.576 1.8 ## targetGroupsGoals.socneeds. -0.02 0.15 0.43 0.569 1.5 ## targetGroupsGoals.socgroups. 0.09 0.04 0.56 0.441 1.4 ## targetGroupsGoals.improve. 0.12 0.29 0.31 0.686 4.8 ## targetGroupsGoals.empower. 0.08 0.05 0.42 0.582 2.0 ## targetGroupsGoals.diversity. 0.05 0.01 0.45 0.545 2.6 ## concepts.pub. 0.09 0.14 0.22 0.777 1.7 ## concepts.data. 0.07 0.08 0.41 0.587 1.4 ## concepts.code. -0.03 -0.08 0.28 0.721 1.6 ## concepts.infra. 0.00 0.19 0.16 0.842 2.4 ## concepts.review. 0.02 0.21 0.20 0.796 3.7 ## impactTargetGroup.pub. -0.06 0.06 0.65 0.346 1.9 ## impactTargetGroup.busi. 0.11 -0.03 0.53 0.469 1.2 ## impactTargetGroup.socgr. 0.00 -0.12 0.58 0.421 1.2 ## impactTargetGroup.welfare. -0.09 0.16 0.45 0.551 4.4 ## impactTargetGroup.civsoc. -0.01 -0.20 0.47 0.534 4.3 ## impactTargetGroup.policy. 0.02 -0.02 0.72 0.285 1.9 ## impactTargetGroup.acad. 0.13 0.12 0.25 0.746 3.5 ## adoptByPolicy.rate. 0.07 0.00 0.42 0.575 1.6 ## Impactstatements.capab. 0.02 0.00 0.69 0.311 1.1 ## Impactstatements.emanc. 0.01 0.02 0.85 0.155 1.0 ## Impactstatements.understanding. -0.01 0.01 0.83 0.173 1.0 ## Impactstatements.mitig. -0.02 -0.01 0.81 0.187 1.0 ## Impactstatements.unknown. -0.03 0.00 0.66 0.341 1.0 ## Impactstatements.unaddressed. -0.11 -0.12 0.27 0.728 2.3 ## dissChannels.peer. 0.03 0.28 0.12 0.883 2.8 ## dissChannels.mono. -0.14 0.14 0.35 0.651 3.4 ## dissChannels.conf. -0.02 0.41 0.20 0.805 1.7 ## dissChannels.policy. 0.01 0.08 0.27 0.734 1.2 ## dissChannels.trad. -0.07 0.09 0.36 0.641 2.1 ## dissChannels.prof. 0.04 0.19 0.37 0.629 4.0 ## dissChannels.web. -0.06 0.00 0.23 0.770 1.6 ## dissChannels.socmed. 0.03 -0.16 0.23 0.772 2.0 ## dissChannels.platf. 0.04 -0.32 0.34 0.659 2.3 ## dissChannels.consult. 0.02 -0.09 0.17 0.830 4.3 ## dissChannels.events. 0.01 0.03 0.40 0.598 2.5 ## dissChannels.public. -0.15 0.01 0.27 0.731 3.0 ## scalabilityRating.up. -0.07 -0.11 0.51 0.490 1.1 ## scalabilityRating.out. 0.08 0.06 0.77 0.232 1.1 ## scalabilityRating.deep. 0.06 -0.01 0.53 0.472 1.3 ## ## MR2 MR1 MR7 MR3 MR6 MR9 MR5 MR4 MR10 MR8 ## SS loadings 4.53 4.00 2.64 2.17 2.53 2.49 2.01 1.38 1.28 1.12 ## Proportion Var 0.09 0.08 0.05 0.04 0.05 0.05 0.04 0.03 0.02 0.02 ## Cumulative Var 0.09 0.16 0.21 0.25 0.30 0.35 0.38 0.41 0.43 0.46 ## Proportion Explained 0.19 0.17 0.11 0.09 0.10 0.10 0.08 0.06 0.05 0.05 ## Cumulative Proportion 0.19 0.35 0.46 0.55 0.66 0.76 0.84 0.90 0.95 1.00 ## ## With factor correlations of ## MR2 MR1 MR7 MR3 MR6 MR9 MR5 MR4 MR10 MR8 ## MR2 1.00 -0.12 -0.07 0.02 -0.14 -0.06 0.30 -0.02 0.05 -0.03 ## MR1 -0.12 1.00 0.25 0.02 0.39 0.41 -0.09 0.01 0.06 0.12 ## MR7 -0.07 0.25 1.00 0.19 0.29 0.29 -0.07 -0.12 0.07 0.14 ## MR3 0.02 0.02 0.19 1.00 0.01 0.06 0.01 0.07 0.04 0.09 ## MR6 -0.14 0.39 0.29 0.01 1.00 0.32 -0.14 0.03 0.11 0.03 ## MR9 -0.06 0.41 0.29 0.06 0.32 1.00 -0.03 -0.09 0.21 0.17 ## MR5 0.30 -0.09 -0.07 0.01 -0.14 -0.03 1.00 0.12 0.04 0.00 ## MR4 -0.02 0.01 -0.12 0.07 0.03 -0.09 0.12 1.00 -0.11 -0.05 ## MR10 0.05 0.06 0.07 0.04 0.11 0.21 0.04 -0.11 1.00 0.05 ## MR8 -0.03 0.12 0.14 0.09 0.03 0.17 0.00 -0.05 0.05 1.00 ## ## Mean item complexity = 2.2 ## Test of the hypothesis that 10 factors are sufficient. ## ## The degrees of freedom for the null model are 1378 and the objective function was 46.29 with Chi Square of 15808.71 ## The degrees of freedom for the model are 893 and the objective function was 26.19 ## ## The root mean square of the residuals (RMSR) is 0.03 ## The df corrected root mean square of the residuals is 0.04 ## ## The harmonic number of observations is 274 with the empirical chi square 911.36 with prob < 0.33 ## The total number of observations was 361 with Likelihood Chi Square = 8770.49 with prob < 0 ## ## Tucker Lewis Index of factoring reliability = 0.139 ## RMSEA index = 0.156 and the 90 % confidence intervals are 0.154 0.16 ## BIC = 3511.73 ## Fit based upon off diagonal values = 0.97 ``` --- ``` ## lavaan 0.6-9 did NOT end normally after 2822 iterations ## ** WARNING ** Estimates below are most likely unreliable ## ## Estimator ML ## Optimization method NLMINB ## Number of model parameters 112 ## ## Used Total ## Number of observations 56 361 ## ## Model Test User Model: ## ## Test statistic NA ## Degrees of freedom NA ## ## Parameter Estimates: ## ## Standard errors Standard ## Information Expected ## Information saturated (h1) model Structured ## ## Latent Variables: ## Estimate Std.Err z-value P(>|z|) Std.lv Std.all ## F1 =~ ## grpsInvlvd.cv. 1.000 0.321 0.471 ## grpsInvlvd.ct. 1.868 NA 0.600 0.727 ## grpsInvlvd.wl. 1.490 NA 0.478 0.582 ## trgtGrpsGls.s. 1.005 NA 0.323 0.646 ## trgtGrpsGls.s. 0.836 NA 0.268 0.549 ## trgtGrpsGls.m. 0.918 NA 0.295 0.631 ## F2 =~ ## adptByPlcy.rt. 1.000 11.672 0.472 ## Impctsttmnts.. 2.155 NA 25.155 0.869 ## Impctsttmnts.. 2.159 NA 25.201 0.854 ## Impctsttmnts.. 1.942 NA 22.668 0.735 ## Impctsttmnts.. 1.984 NA 23.159 0.697 ## Impctsttmnts.. 1.013 NA 11.829 0.557 ## Impctsttmnts.. 0.390 NA 4.547 0.263 ## F3 =~ ## concepts.pub. 1.000 0.001 0.003 ## concepts.data. 307.260 NA 0.254 0.514 ## concepts.code. 421.179 NA 0.349 0.746 ## concepts.infr. 352.026 NA 0.291 0.634 ## dssChnnls.trd. 137.625 NA 0.114 0.228 ## dissChnnls.wb. 114.408 NA 0.095 0.258 ## dssChnnls.plt. 320.367 NA 0.265 0.577 ## F4 =~ ## trgtGrpsGls.d. 1.000 0.460 1.000 ## F5 =~ ## sclbltyRtng.p. 1.000 20.785 0.651 ## sclbltyRtng.t. 1.524 NA 31.669 0.866 ## sclbltyRtng.d. 1.134 NA 23.571 0.606 ## F6 =~ ## grpsInvlvd.pl. 1.000 0.481 0.623 ## impctTrgtGrp.. 6.865 NA 3.305 0.985 ## dssChnnls.plc. 0.409 NA 0.197 0.455 ## F7 =~ ## trnsdscplnrE.. 1.000 0.001 0.000 ## grpsInvlvd.bs. -782.065 NA -0.447 -0.594 ## impctTrgtGrp.. -2637.459 NA -1.507 -0.548 ## impctTrgtGrp.. -4739.693 NA -2.708 -0.791 ## F8 =~ ## dssChnnls.cnf. 1.000 0.225 1.000 ## F9 =~ ## contribTSI.rt. 1.000 2.348 1.000 ## F10 =~ ## motivatin.phn. 1.000 0.036 0.019 ## motivatin.prb. 200.929 NA 7.259 3.474 ## ## Covariances: ## Estimate Std.Err z-value P(>|z|) Std.lv Std.all ## F1 ~~ ## F2 -1.055 NA -0.281 -0.281 ## F3 -0.000 NA -0.131 -0.131 ## F4 0.090 NA 0.613 0.613 ## F5 -1.992 NA -0.299 -0.299 ## F6 0.059 NA 0.380 0.380 ## F7 -0.000 NA -0.174 -0.174 ## F8 0.011 NA 0.154 0.154 ## F9 0.342 NA 0.454 0.454 ## F10 0.001 NA 0.054 0.054 ## F2 ~~ ## F3 0.003 NA 0.267 0.267 ## F4 -0.766 NA -0.143 -0.143 ## F5 96.911 NA 0.399 0.399 ## F6 -0.099 NA -0.018 -0.018 ## F7 0.000 NA 0.031 0.031 ## F8 -0.549 NA -0.209 -0.209 ## F9 7.612 NA 0.278 0.278 ## F10 -0.010 NA -0.024 -0.024 ## F3 ~~ ## F4 0.000 NA 0.149 0.149 ## F5 -0.001 NA -0.070 -0.070 ## F6 -0.000 NA -0.087 -0.087 ## F7 -0.000 NA -0.488 -0.488 ## F8 -0.000 NA -0.084 -0.084 ## F9 -0.000 NA -0.141 -0.141 ## F10 -0.000 NA -0.063 -0.063 ## F4 ~~ ## F5 -2.540 NA -0.266 -0.266 ## F6 0.068 NA 0.309 0.309 ## F7 -0.000 NA -0.034 -0.034 ## F8 0.002 NA 0.015 0.015 ## F9 0.126 NA 0.117 0.117 ## F10 -0.001 NA -0.053 -0.053 ## F5 ~~ ## F6 -1.508 NA -0.151 -0.151 ## F7 -0.001 NA -0.063 -0.063 ## F8 -0.546 NA -0.117 -0.117 ## F9 -0.702 NA -0.014 -0.014 ## F10 0.045 NA 0.059 0.059 ## F6 ~~ ## F7 -0.000 NA -0.251 -0.251 ## F8 -0.032 NA -0.298 -0.298 ## F9 0.457 NA 0.404 0.404 ## F10 0.000 NA 0.002 0.002 ## F7 ~~ ## F8 0.000 NA 0.119 0.119 ## F9 0.000 NA 0.058 0.058 ## F10 -0.000 NA -0.033 -0.033 ## F8 ~~ ## F9 -0.034 NA -0.065 -0.065 ## F10 -0.000 NA -0.033 -0.033 ## F9 ~~ ## F10 0.009 NA 0.101 0.101 ## ## Variances: ## Estimate Std.Err z-value P(>|z|) Std.lv Std.all ## .grpsInvlvd.cv. 0.361 NA 0.361 0.778 ## .grpsInvlvd.ct. 0.321 NA 0.321 0.472 ## .grpsInvlvd.wl. 0.447 NA 0.447 0.662 ## .trgtGrpsGls.s. 0.146 NA 0.146 0.583 ## .trgtGrpsGls.s. 0.167 NA 0.167 0.698 ## .trgtGrpsGls.m. 0.131 NA 0.131 0.602 ## .adptByPlcy.rt. 475.833 NA 475.833 0.777 ## .Impctsttmnts.. 205.943 NA 205.943 0.246 ## .Impctsttmnts.. 234.975 NA 234.975 0.270 ## .Impctsttmnts.. 436.456 NA 436.456 0.459 ## .Impctsttmnts.. 568.600 NA 568.600 0.515 ## .Impctsttmnts.. 311.040 NA 311.040 0.690 ## .Impctsttmnts.. 277.578 NA 277.578 0.931 ## .concepts.pub. 0.081 NA 0.081 1.000 ## .concepts.data. 0.180 NA 0.180 0.736 ## .concepts.code. 0.097 NA 0.097 0.443 ## .concepts.infr. 0.127 NA 0.127 0.598 ## .dssChnnls.trd. 0.237 NA 0.237 0.948 ## .dissChnnls.wb. 0.126 NA 0.126 0.934 ## .dssChnnls.plt. 0.141 NA 0.141 0.667 ## .trgtGrpsGls.d. 0.000 0.000 0.000 ## .sclbltyRtng.p. 587.381 NA 587.381 0.576 ## .sclbltyRtng.t. 335.072 NA 335.072 0.250 ## .sclbltyRtng.d. 957.568 NA 957.568 0.633 ## .grpsInvlvd.pl. 0.365 NA 0.365 0.612 ## .impctTrgtGrp.. 0.330 NA 0.330 0.029 ## .dssChnnls.plc. 0.149 NA 0.149 0.793 ## .trnsdscplnrE.. 3.732 NA 3.732 1.000 ## .grpsInvlvd.bs. 0.367 NA 0.367 0.648 ## .impctTrgtGrp.. 5.288 NA 5.288 0.700 ## .impctTrgtGrp.. 4.377 NA 4.377 0.374 ## .dssChnnls.cnf. 0.000 0.000 0.000 ## .contribTSI.rt. 0.000 0.000 0.000 ## .motivatin.phn. 3.666 NA 3.666 1.000 ## .motivatin.prb. -48.331 NA -48.331 -11.067 ## F1 0.103 NA 1.000 1.000 ## F2 136.234 NA 1.000 1.000 ## F3 0.000 NA 1.000 1.000 ## F4 0.211 NA 1.000 1.000 ## F5 432.011 NA 1.000 1.000 ## F6 0.232 NA 1.000 1.000 ## F7 0.000 NA 1.000 1.000 ## F8 0.051 NA 1.000 1.000 ## F9 5.515 NA 1.000 1.000 ## F10 0.001 NA 1.000 1.000 ```